Course title  Advanced Data Analysis Software Development with R  
Assesment method  Exam  Hours/semester  60  Lect.  Exercises  Lab.  Project  
ETCS  Year  Hours/week  2  2  
Prerequisites  
Basic knowledge of algorithms, data structures and R programming corresponding to introductory level classes in these topics is assumed.  
Course description  
R is a de facto standard language and environment for statistical computing, data analysis, and graphics. This course subjects students to the depth and breadth of advanced, stateoftheart R programming practice.  
Course objectives  
The students' theoretical knowledge of data analysis, machine learning, and other computational methods often does not go handinhand with their abilities to implement such algorithms on their own. The main aim of this very module is to fill this gap, so that the students shall have necessary skills to develop high quality software for their own scientific or any other purposes, but also to share it within the user community, via peerreviewed R package repositories like CRAN or Bioconductor.  
Skills  
By completing the course, the students should be able to:


Grading  
6 homework assignments, a couple of tasks each (60%) Final exam, written (40%) >50% to pass. 

Reference Texts and Software  
Books: [1] Gągolewski M., Programowanie w języku R, Wydawnictwo Naukowe PWN, 2014 (in Polish). [2] Chambers J.M., Programming with Data, Springer, 1998. [3] Chambers J.M., Software for Data Analysis. Programming with R, Springer, 2008. [4] Venables W.N., Ripley B.D., S Programming, Springer, 2000. [5] Eddelbuettel D., Seamless R and C++ Integration with Rcpp. Springer, 2013. [6] Wickham H., Advanced R, Chapman and Hall, 2014. [7] Matloff N., The art of R programming, No Starch Press, 2011. Software:


Lecture Schedule  
1. 
R basics (part I)


2. 
R basics (part II)


3. 
R basics (part III)


4. 
Character string processing


5. 
File processing


6. 
Advanced R programming


7. 
Rcpp (part I)


8. 
Rcpp (part II)
